調査、論文、教育、資格学習（4,125 件）
概要と使いどころ
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。
visionOS widget patterns including mounting styles, glass/paper textures, proximity-aware layouts, and spatial widget families. Use when creating or adapting widgets for visionOS.
日本語の概要は準備中です。原文の説明を表示しています。
Deep competitive analysis for iOS/macOS apps including feature comparison, pricing analysis, strengths/weaknesses, market positioning, and differentiation opportunities. Use when user asks for competitive analysis, competitor research, feature comparison, market positioning, or wants to understand competition in detail.
日本語の概要は準備中です。原文の説明を表示しています。
Deep market analysis for iOS/macOS apps including market sizing (TAM/SAM/SOM), growth trends, market maturity, entry barriers, distribution channels, and revenue potential. Use when user asks for market research, market size, market opportunity, growth potential, TAM/SAM/SOM, or market trends.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user wants to optimize configurable system parameters against a measurable scalar objective, especially for model training, inference, quantitative strategies, reinforcement learning, scientific workflows, or other expensive black-box evaluations where reading the project can improve trial selection.
日本語の概要は準備中です。原文の説明を表示しています。
ProteoDisco is an R package to facilitate proteogenomics studies. It houses functions to create customized (variant) protein databases based on user-submitted genomic variants, splice-junctions, fusion genes and manual transcript sequences. The flexible workflow can be adopted to suit a myriad of research and experimental settings.
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A package that provides a client interface to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST API. Only for academic use by academic users belonging to academic institutions (see <https://www.kegg.jp/kegg/rest/>). Note that KEGGREST
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A post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure with semi-supervised learning algorithm AdaSampling technique. The current version of scReClassify supports Support Vector Machine and Random Forest as a base classifier.
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Manhattan plot and QQ Plot are commonly used to visualize the end result of Genome Wide Association Study. The "ggmanh" package aims to keep the generation of these plots simple while maintaining customizability. Main functions include manhattan_plot, qqunif, and thinPoints.
日本語の概要は準備中です。原文の説明を表示しています。
Codex-only — use when the user asks to run claude-smart commands in Codex, including claude-smart show, learn, restart, dashboard, clear-all, or slash-like requests such as /claude-smart:learn. In Claude Code the native plugin slash commands handle these; do not invoke this skill there.
日本語の概要は準備中です。原文の説明を表示しています。
Use when creating tailored resumes for job applications - researches company/role, creates optimized templates, conducts branching experience discovery to surface undocumented skills, and generates professional multi-format resumes from user's resume library while maintaining factual integrity
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带用户精读一篇文章/文档并真正学透（不是出摘要）。五步：抽干货 → 切讲次 → 每讲跑「学-考-讲」循环 → 对号入座 → 实操+测验+讲错题+蒸馏。每讲产出 HTML 课件 + 笔记落盘；讲完必考一次，用户复述后必须挑不精确处拧紧；抽象概念上可交互演示（能点能跑）；全程用用户自己的业务场景当案例；学完出多题型自动判分测验卷，最后把收获蒸馏回用户的工具。核心触发条件是用户要的是"学会"而不是"要一份结果"，例如"我们一起学这篇文章/这个链接"、"带我学"、"精读"、"我想学会 X"、"这篇我看不懂你给我讲讲"。有具体材料（链接、本地文件、PDF，或用户自己的 skill/文档/代码）时直接开跑；只有学习意图而没材料时仍走本 skill，但开工第一件事是和用户一起把材料定下来，禁止凭记忆开讲。不适用于：只要一份总结/摘要/教程长文，用户读完就完、不需要答题（用 readable-output）、只是搜集资料做调研（使用可用的网页/平台取材工具）、帮我写 PRD/测试用例（用 prd-test-writer）、以及用户其实是想让你直接把活干了（那就直接做）。
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系统化学习材料生成器。给一个新领域/技术/概念，AI 自主调研、搭体系、产出 HTML 学习材料（含骨架/案例/工程化/争议+盲区）。当用户说"我要学 X / 帮我系统拆解 X / 我想吃透 X 这个领域 / 给我整理 X 的全貌 / 深度调研 X 给我系统讲讲 / 把 X 搞透"时触发。不适用于"X 行不行/为什么 Y"（使用可用的调研工具直接研究）、"X 有哪些好玩案例"（用 case-radar 给散点）、"把这堆素材整理成 HTML"（用 readable-output 处理已有素材）、文章写作（用 writing-assistant）、整页界面方案（用 page-solution-design）。
日本語の概要は準備中です。原文の説明を表示しています。
Digest of the most interesting new posts on [REPLACE: TOPIC] from RSS feeds and the open web
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Evolve a skill by generating variations, evaluating them, and updating the best version
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Cross-platform social research - narrative-first intelligence on what people are saying about a topic across Reddit, X, HN, Polymarket, and the web over the last 30 days
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Build prospect lists where the prospect is already one hop from the client, and write the line that says so. Seven members, each with its own source. Current-customer alumni is the priority member - everyone who ever worked at one of the client's customers or case-study companies and has since moved on. Triggers on "warm intro", "who do we know at that account", "they used to work at our customer", "alumni of our case study company", "shared investor angle", "who is in the same city as our rep", "did they visit our site". Outputs warm_intro_line plus a per-member evidence column.
日本語の概要は準備中です。原文の説明を表示しています。
Autonomous cold email campaign launcher. Takes one target company domain, scrapes their website, generates an ICP with Claude, pulls matching leads via Prospeo, enriches emails + company descriptions, personalizes each lead with parallel Claude Code Task sub-agents (A/B/C variants), and uploads as a live Smartlead campaign. Uses local JSON files for state (no database required). Use for automated daily campaign launches after you have an initial `client-profile.yaml` from /icp-onboarding. Triggers on "auto-research", "launch an automated campaign", "daily campaign", "run the research loop".
日本語の概要は準備中です。原文の説明を表示しています。
Turns one customer case study into the tightest list of companies that would read that case study and see themselves. Decomposes WHY the case-study company resonates into concrete attributes, translates those attributes into database filters, intersects them with a lookalike anchor, then widens with a model judge. Triggers on "find more companies like our best customer", "who would our case study land with", "lookalikes of X", "build a list around this case study", "companies like our happiest client", "expand this list, it is too small". Outputs a filter set plus one merge field naming which case study to cite.
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Finds a prospect company's own case-study or customer-stories page and pulls one named customer off it, so email 1 can open with "saw your work with Intercom". Triggers on "case study", "customer stories", "who are their clients", "saw your work with X", "name-drop one of their customers", "logos on their site". Outputs case_study_line, a short clause that finishes the sentence "Saw ...".
日本語の概要は準備中です。原文の説明を表示しています。
Full-spectrum Amazon competitor analysis. Compare listings, pricing, reviews, advertising strategy, and market positioning against direct competitors. Identify weaknesses to exploit and strengths to counter. Use when the user asks about competitor analysis, competitive research, ASIN comparison, pricing analysis, or how to beat competitors on Amazon.
日本語の概要は準備中です。原文の説明を表示しています。
Profitable niche discovery for Amazon sellers. Identifies underserved markets with high demand but low competition. Analyzes market gaps, emerging segments, and untapped opportunities across categories. Use when the user asks about finding niches, profitable markets, underserved segments, market gaps, blue ocean opportunities, niche research, or what markets to enter on Amazon.
日本語の概要は準備中です。原文の説明を表示しています。